Intelligent document processing for logistics
An OCR and machine-learning pipeline that reads bills of lading, invoices and shipping records, including handwritten fields, and feeds them into existing logistics software.
See also: Intelligent document processing for logistics and freight
The brief
A logistics and transportation operator handled bills of lading, invoices and shipping records by hand, many of them containing handwritten details. Manual entry was slow and error-prone.
They needed to automate document handling while integrating cleanly into their existing logistics software, without disrupting day-to-day operations.
- Automate handling of bills of lading, invoices and shipping records.
- Read handwritten as well as printed fields.
- Adapt to new document formats over time.
- Cross-check extracted data against existing records.
- Integrate with existing logistics software without disrupting operations.
Our AI-native approach
We combined advanced OCR with AI analysis to convert and interpret document images, including handwritten fields, and added machine learning that continuously adapts to new document formats.
Extracted data is validated automatically against existing records, and a predictive analytics layer uses historical data to suggest optimised delivery routes.
What we built
Document intake
Bills of lading, invoices and shipping records are captured as document images.
OCR & AI analysis
Document images are converted and interpreted, including handwritten fields.
Adaptive machine learning
Models continuously adapt as new document formats appear.
Automated validation
Extracted data is cross-checked against existing records.
Predictive route analytics
Historical data is used to suggest optimised delivery routes.
System integration
Extracted data flows into the operator's existing logistics software.
How we built it
Integration was a first-order constraint: the pipeline had to slot into the operator's existing logistics software without disrupting operations.
Machine learning that adapts to new document formats means the system keeps pace as the document mix changes, rather than needing to be rebuilt for each new layout.
How it works
- Step 01
Ingest
Document images are captured.
- Step 02
Extract
OCR and AI analysis read printed and handwritten fields.
- Step 03
Adapt
ML learns new document formats over time.
- Step 04
Validate
Extracted data is cross-checked against existing records.
- Step 05
Integrate
Data flows into existing logistics systems and route analytics.
The extraction stack pairs advanced OCR with AI analysis capable of interpreting handwritten fields, backed by machine learning that continuously adapts to new document formats.
On top of the document data, a predictive analytics layer draws on historical records to suggest optimised delivery routes, supporting more efficient logistics planning.
The impact
Significant reduction in manual data entry.
Improved accuracy on handwritten and complex data.
Seamless integration with existing systems.
Predictive analytics supporting more efficient logistics planning.
Technology stack
Related service: Document AI & Intelligent Document Processing.
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